# In[11]:
from Augmentor import Pipeline
# In[12]:
augmentor = Pipeline('/home/asherif844/sparkNotebooks/Ch03/MNIST/images')
# In[13]:
augmentor.rotate(probability=0.9, max_left_rotation=25, max_right_rotation=25)
# In[14]:
for i in range(1, 3):
augmentor.sample(10)
# In[15]:
xtrain = data.train.images
ytrain = np.asarray(data.train.labels)
xtest = data.test.images
ytest = np.asarray(data.test.labels)
# In[16]:
xtrain = xtrain.reshape(xtrain.shape[0], 28, 28, 1)
xtest = xtest.reshape(xtest.shape[0], 28, 28, 1)
ytest = ytest.reshape(ytest.shape[0], 10)
ytrain = ytrain.reshape(ytrain.shape[0], 10)
# p.flip_top_bottom(probability=0.5)
#
# # 随机90， 180， 270 度旋转
# p.rotate_random_90(probability=0.75)
#
# # 随机20度内旋转，不变形, 四角填充黑色，图片大小不变
# p.rotate_without_crop(probability=0.5, max_left_rotation=20, max_right_rotation=20)
#
# # 随机20度内旋转，不变形, 四角填充黑色，图片大小调整
# p.rotate_without_crop(probability=0.5, max_left_rotation=20, max_right_rotation=20, expand=True)
#
# # 随机剪切, 中心不变
# p.crop_by_size(probability=0.5, width=100, height=100)
#
# # 随机剪切，中心变化，可调节剪切比例
# p.crop_random(probability=0.5, percentage_area=0.8)
#
# # 随机20%缩放，大小不变，图片缩小时以黑色填充
# p.zoom(probability=0.5, min_factor=0.8, max_factor=1.2)
# resize
p.resize(probability=1, width=256, height=256)
# execute and sample from the pipeline
time1 = time.time()
num_of_samples = 14200
p.sample(num_of_samples)
time2 = time.time()
print("time cost = " + str(time2 - time1) + "s")
